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Dimensional Reduction of Adaptively Refined Nonlinear Computational Models.
Dimensional Reduction of Adaptively Refined Nonlinear Computational Models.
- 자료유형
- 학위논문
- Control Number
- 0017163732
- International Standard Book Number
- 9798342113557
- Dewey Decimal Classification Number
- 530
- Main Entry-Personal Name
- Little, Clayton.
- Publication, Distribution, etc. (Imprint
- [S.l.] : Stanford University., 2024
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- Physical Description
- 134 p.
- General Note
- Source: Dissertations Abstracts International, Volume: 86-04, Section: A.
- General Note
- Advisor: Farhat, Charbel.
- Dissertation Note
- Thesis (Ph.D.)--Stanford University, 2024.
- Summary, Etc.
- 요약Adaptive mesh refinement (AMR) is fairly practiced in the context of high-dimensional, mesh-based computational models. However, it is preliminary in the context of low-dimensional, generalized-coordinate-based computational models such as projection-based reduced-order models (PROMs). This dissertation presents a complete framework for projection-based model order reduction (PMOR) of nonlinear problems in the presence of AMR that builds on elements from existing methods and augments them with critical new contributions. In particular, it proposes two algorithms for computing an inner product between spatially-adapted solution snapshots for the purpose of clustering and PMOR. The first algorithm is a semi-analytical pseudo-meshless inner product which builds on existing methods, and the second algorithm is a novel approximate method which maximizes computational efficiency. The proposed framework exploits hyperreduction---specifically, the energy-conserving sampling and weighting hyperreduction (ECSW) method---to deliver for nonlinear and/or parametric problems the desired computational gains. Most importantly, it exploits piecewise-affine approximation of the solution manifold to make the most of the notion of a supermesh, while achieving computational tractability. The performance of the proposed framework for PMOR in the presence of AMR is assessed for computational fluid dynamics (CFD) applications utilizing AMR. Its significance is demonstrated by the reported accuracies and gains in computational efficiency.
- Subject Added Entry-Topical Term
- Vortices.
- Subject Added Entry-Topical Term
- Fluid-structure interaction.
- Subject Added Entry-Topical Term
- Mathematical models.
- Subject Added Entry-Topical Term
- Fluid dynamics.
- Subject Added Entry-Topical Term
- Symmetry.
- Subject Added Entry-Topical Term
- Decomposition.
- Subject Added Entry-Topical Term
- Mechanics.
- Subject Added Entry-Topical Term
- Dynamical systems.
- Subject Added Entry-Topical Term
- Design optimization.
- Subject Added Entry-Topical Term
- Physics.
- Subject Added Entry-Topical Term
- Partial differential equations.
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- Viscosity.
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- Turbulence models.
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- Digital twins.
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- Neural networks.
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- Reynolds number.
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- Computer engineering.
- Subject Added Entry-Topical Term
- Design.
- Subject Added Entry-Topical Term
- Fluid mechanics.
- Added Entry-Corporate Name
- Stanford University.
- Host Item Entry
- Dissertations Abstracts International. 86-04A.
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:655065
